Abstract
We propose a method of tool use considering the transition process of a body model from not grasping to grasping a tool using a single model. In our previous research, we proposed a tool-body assimilation model in which a robot autonomously learns tool functions using a deep neural network (DNN) and recurrent neural network (RNN) through experiences of motor babbling. However, the robot started its motion already holding the tools. In real-life situations, the robot would make decisions regarding grasping (handling) or not grasping (manipulating) a tool. To achieve this, the robot performs motor babbling without the tool pre-attached to the hand with the same motion twice, in which the robot handles the tool or manipulates without graping it. To evaluate the model, we have the robot generate motions with showing the initial and target states. As a result, the robot could generate the correct motions with grasping decisions.
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Acknowledgment
This work has been supported by JSPS Grant-in-Aid for Scientific Research 15J12683; the Program for Leading Graduate Schools, “Graduate Program for Embodiment Informatics” of the Ministry of Education, Culture, Sports, Science, and Technology; JSPS Grant-in-Aid for Scientific Research (S) (2522005); “Fundamental Study for Intelligent Machine to Coexist with Nature” Research Institute for Science and Engineering, Waseda University; MEXT Grant-in-Aid for Scientific Research (A) 15H01710; and MEXT Grant-in-Aid for Scientific Research on Innovative Areas “Constructive Developmental Science” (24119003)
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Takahashi, K., Tjandra, H., Ogata, T., Sugano, S. (2016). Body Model Transition by Tool Grasping During Motor Babbling Using Deep Learning and RNN. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_20
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DOI: https://doi.org/10.1007/978-3-319-44778-0_20
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